我开始研究应用于计算机视觉和情感计算的机器学习和贝叶斯推理。
如果我理解正确的话，
- 经典 IA、本体论、语义网络研究人员
- 以及机器学习和贝叶斯研究人员
之间有一个大讨论，我认为这通常被称为强人工智能与弱人工智能，也与功能心理学（大脑作为黑匣子）等哲学问题有关集）和认知心理学（心智理论，镜像神经元），但这不是像这样的编程论坛的重点。
我想了解两种观点之间的差异。理想情况下，答案将参考示例和学术论文，其中一种方法取得良好结果，另一种则失败。我也对历史趋势感兴趣：为什么方法不再受欢迎而更新的方法开始兴起。例如，我知道贝叶斯推理在计算上很困难，是NP中的问题，这就是为什么很长一段时间概率模型在信息技术世界中不受青睐。然而，他们在计量经济学领域开始崛起。
I'm starting to study machine learning and bayesian inference applied to computer vision and affective computing.
If I understand right, there is a big discussion between
- classical IA, ontology, semantic web researchers
- and machine learning and bayesian guys
I think it is usually referred as strong AI vs weak AI related also to philosophical issues like functional psychology (brain as black box set) and cognitive psychology (theory of mind, mirror neuron), but this is not the point in a programming forum like this.
I'd like to understand the differences between the two points of view. Ideally, answers will reference examples and academic papers where one approach get good results and the other fails. I am also interested in the historical trends: why approaches fell out of favour and a newer approaches began to rise up. For example, I know that Bayesian inference is computationally intractable, problem in NP, and that's why for a long time probabilistic models was not favoured in information technology world. However, they've began to rise up in econometrics.
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我认为你把几个想法混在一起了。确实，“人工智能”任务的基于规则的方法和概率方法之间存在区别，但它与人工智能的强弱无关，与心理学关系不大，而且也不像两方之间的战斗。另外，我认为说贝叶斯推理没有在计算机科学中使用，因为推理通常是 NP 完全的，这有点误导。在实践中，这个结果通常并不重要，而且大多数机器学习算法无论如何都不会进行真正的贝叶斯推理。
话虽如此，自然语言处理的历史从 80 年代和 90 年代初的基于规则的系统到今天的机器学习系统。查看MUC 会议的历史，了解信息提取任务的早期方法。将其与当前最先进的命名实体识别和解析（ ACL wiki 是一个很好的来源），它们都基于机器学习方法。
就具体参考文献而言，我怀疑您会发现有人写学术论文说“统计系统比基于规则的系统更好”，因为通常很难做出这样的明确陈述。快速谷歌搜索“统计与基于规则”，会得到类似this<的论文/a> 着眼于机器翻译，并根据其优点和缺点建议使用这两种方法。我想你会发现这是非常典型的学术论文。我读过的唯一真正在这个问题上表明立场的是'数据的不合理有效性'，这是一本很好的读物。
I think you have got several ideas mixed up together. It's true that there is a distinction that gets drawn between rule-based and probabilistic approaches to 'AI' tasks, however it has nothing to do with strong or weak AI, very little to do with psychology and it's not nearly as clear cut as being a battle between two opposing sides. Also, I think saying Bayesian inference was not used in computer science because inference is NP complete in general is a bit misleading. That result often doesn't matter that much in practice and most machine learning algorithms don't do real Bayesian inference anyway.
Having said all that, the history of Natural Language Processing went from rule-based systems in the 80s and early 90s to machine learning systems up to the present day. Look at the history of the MUC conferences to see the early approaches to information extraction task. Compare that with the current state-of-the-art in named entity recognition and parsing (the ACL wiki is a good source for this) which are all based on machine learning methods.
As far as specific references, I doubt you'll find anyone writing an academic paper that says 'statistical systems are better than rule-based systems' because it's often very hard to make a definite statement like that. A quick Google for 'statistical vs. rule based' yields papers like this which looks at machine translation and recommends using both approaches, according to their strengths and weaknesses. I think you'll find that this is pretty typical of academic papers. The only thing I've read that really makes a stand on the issue is 'The Unreasonable Effectiveness of Data' which is a good read.
至于“基于规则”与“概率”的问题，你可以去读 Judea Pearl 的经典著作——《智能系统中的概率推理》。Pearl 的著作非常偏向他所谓的“内涵系统”，这基本上是反面的。我认为这本书引发了人工智能中的整个概率问题（你也可以说时间已经到了，但
我认为机器学习是一本不同 的书）。故事（尽管它更接近概率人工智能而不是逻辑）。
As for the "rule-based" vs. " probabilistic" thing you can go for the classic book by Judea Pearl - "Probabilistic Reasoning in Intelligent Systems. Pearl writes very biased towards what he calls "intensional systems" which is basically the counter-part to rule-based stuff. I think this book is what set off the whole probabilistic thing in AI (you can also argue the time was due, but then it was THE book of that time).
I think machine-learning is a different story (though it's nearer to probabilistic AI than to logics).